Citations

SummaryObjectiveWe aimed to determine the frequency of probable obstructive sleep apnea (pOSA) in refractory epilepsy monitoring unit inpatients and clinical features associated with pOSA, including risk for sudden unexpected death in epilepsy (SUDEP).MethodsWe prospectively recruited 49 consecutive adult patients admitted to the Mayo Clinic Epilepsy Monitoring Unit with focal, generalized, or unclassified epilepsy syndromes. pOSA was identified using oximetric oxyhemoglobin desaturation index (ODI) and the Sleep Apnea–Sleep Disorders Questionnaire (SA‐SDQ) and STOP‐BAG screening tools. Revised SUDEP Risk Inventory (rSUDEP‐7) scores were calculated, and epilepsy patients with and without pOSA were compared with Wilcoxon signed‐rank tests. Correlation and regression analyses were utilized to determine relationships between pOSA and rSUDEP‐7 scores.ResultsThirty‐five percent of patients had pOSA, with a mean ODI of 11.3 ± 5.1/h (range = 5.1‐22.8). Patients with pOSA were older and heavier, and more frequently had a focal epilepsy syndrome and longer epilepsy duration, with higher SA‐SDQ and STOP‐BAG scores (all P < 0.05). Median rSUDEP‐7 score was 3 ± 1.4 (range = 0‐6). Higher rSUDEP‐7 scores were positively correlated with higher ODI (P = 0.036). rSUDEP‐7 score ≥ 5 was associated with pOSA by ODI, SA‐SDQ, and STOP‐BAG questionnaire criteria (P < 0.05).SignificanceOur pilot study identified a high frequency of pOSA in refractory epilepsy monitoring patients, finding that pOSA patients were older and heavier, with higher screening symptoms for sleep apnea and more frequent focal seizures with a longer epilepsy duration. We also found a possible association between OSA and SUDEP risk. Identification and treatment of OSA in patients with epilepsy could conceivably provide a novel approach toward preventing the risk of SUDEP. Future studies with polysomnography are needed to confirm predictive features for OSA in epilepsy populations, and to determine whether OSA is associated with SUDEP risk.

Abstract Objective This study investigates high‐frequency oscillations ( HFO s; 65–600 Hz) as a biomarker of epileptogenic brain and explores three barriers to their clinical translation: (1) Distinguishing pathological HFO s (path HFO ) from physiological HFO s (phys HFO ). (2) Classifying tissue under individual electrodes as epileptogenic (3) Reproducing results across laboratories. Methods We recorded HFO s using intracranial EEG ( iEEG ) in 90 patients with focal epilepsy and 11 patients without epilepsy. In nine patients with epilepsy putative phys HFO s were induced by cognitive or motor tasks. HFO s were identified using validated detectors. A support vector machine ( SVM ) using HFO features was developed to classify tissue under individual electrodes as normal or epileptogenic. Results There was significant overlap in the amplitude, frequency, and duration distributions for spontaneous phys HFO , task induced phys HFO , and path HFO , but the amplitudes of the path HFO were higher ( P < 0.0001). High gamma path HFO had the strongest association with seizure onset zone ( SOZ ), and were elevated on SOZ electrodes in 70% of epilepsy patients ( P < 0.0001). Failure to resect tissue generating high gamma path HFO was associated with poor outcomes ( P < 0.0001). A SVM classified individual electrodes as epileptogenic with 63.9% sensitivity and 73.7% specificity using SOZ as the target. Interpretation A broader range of interictal path HFO (65–600 Hz) than previously recognized are biomarkers of epileptogenic brain, and are associated with SOZ and surgical outcome. Classification of HFO s into physiological or pathological remains challenging. Classification of tissue under individual electrodes was demonstrated to be feasible. The open source data and algorithms provide a resource for future studies.

OBJECTIVE Deep brain stimulation (DBS) of the anterior nucleus of the thalamus (ANT) is a promising therapy for refractory epilepsy. Unfortunately, the variability in outcomes from ANT DBS is not fully understood. In this pilot study, the authors assess potential differences in functional connectivity related to the volume of tissue activated (VTA) in ANT DBS responders and nonresponders as a means for better understanding the mechanism of action and potentially improving DBS targeting. METHODS This retrospective analysis consisted of 6 patients who underwent ANT DBS for refractory epilepsy. Patients were classified as responders (n = 3) if their seizure frequency decreased by at least 50%. The DBS electrodes were localized postoperatively and VTAs were computationally generated based on DBS programming settings. VTAs were used as seed points for resting-state functional MRI connectivity analysis performed using a control dataset. Differences in cortical connectivity to the VTA were assessed between the responder and nonresponder groups. RESULTS The ANT DBS responders showed greater positive connectivity with the default mode network compared to nonresponders, including the posterior cingulate cortex, medial prefrontal cortex, inferior parietal lobule, and precuneus. Interestingly, there was also a consistent anticorrelation with the hippocampus seen in responders that was not present in nonresponders. CONCLUSIONS Based on their pilot study, the authors observed that successful ANT DBS in patients with epilepsy produces increased connectivity in the default mode network, which the authors hypothesize increases the threshold for seizure propagation. Additionally, an inhibitory effect on the hippocampus mediated through increased hippocampal γ-aminobutyric acid (GABA) concentration may contribute to seizure suppression. Future studies are planned to confirm these findings.

When medically intractable epilepsy is multifocal or focal but poorly localized, neuromodulation can be useful therapy. One such technique is deep brain stimulation (DBS) targeting the anterior nucleus of the thalamus (ANT). Unfortunately, the ANT is difficult to visualize in standard MRI sequences and its indirect targeting is difficult because of thalamic variability and atrophy in patients with epilepsy. The following study describes the novel use of the fast gray matter acquisition T1 inversion recovery (FGATIR) MRI sequence to delineate the mammillothalamic tract for direct targeting of the ANT through visualizing the termination of the mammillothalamic tract in the ANT. The day prior to surgery in a 19-year-old, right-handed woman with a 5-year history of epilepsy, MRI was performed on a 3-T Siemens Prisma scanner (Siemens AG, Healthcare Sector) using a 64-channel head and neck coil. As part of the imaging protocol, noncontrast magnetization-prepared rapid gradient echo (MP-RAGE) and diffusion tensor imaging (DTI) sequences were obtained for targeting purposes. The ANT was directly targeted using the FGATIR sequence, and bilateral Medtronic 3389 leads were placed. At the last follow-up (2 months), the patient reported an approximate 75% decrease in seizure frequency, as well as a decrease in seizure severity.

OBJECTIVE Although it is still early in its application, laser interstitial thermal therapy (LiTT) has increasingly been employed as a surgical option for patients with mesial temporal lobe epilepsy. This study aimed to describe mesial temporal lobe ablation volumes and seizure outcomes following LiTT across the Mayo Clinic’s 3 epilepsy surgery centers. METHODS This was a multi-site, single-institution, retrospective review of seizure outcomes and ablation volumes following LiTT for medically intractable mesial temporal lobe epilepsy between October 2011 and October 2015. Pre-ablation and post-ablation follow-up volumes of the hippocampus were measured using FreeSurfer, and the volume of ablated tissue was also measured on intraoperative MRI using a supervised spline-based edge detection algorithm. To determine seizure outcomes, results were compared between those patients who were seizure free and those who continued to experience seizures. RESULTS There were 23 patients who underwent mesial temporal LiTT within the study period. Fifteen patients (65%) had left-sided procedures. The median follow-up was 34 months (range 12–70 months). The mean ablation volume was 6888 mm 3 . Median hippocampal ablation was 65%, with a median amygdala ablation of 43%. At last follow-up, 11 (48%) of these patients were seizure free. There was no correlation between ablation volume and seizure freedom (p = 0.69). There was also no correlation between percent ablation of the amygdala (p = 0.28) or hippocampus (p = 0.82) and seizure outcomes. Twelve patients underwent formal testing with computational visual fields. Visual field changes were seen in 67% of patients who underwent testing. Comparing the 5 patients with clinically noticeable visual field deficits to the rest of the cohort showed no significant difference in ablation volume between those patients with visual field deficits and those without (p = 0.94). There were 11 patients with follow-up neuropsychological testing. Within this group, verbal learning retention was 76% in the patients with left-sided procedures and 89% in those with right-sided procedures. CONCLUSIONS In this study, there was no significant correlation between the ablation volume after LiTT and seizure outcomes. Visual field deficits were common in formally tested patients, much as in patients treated with open temporal lobectomy. Further studies are required to determine the role of amygdalohippocampal ablation.

Abstract Objective . An ability to map seizure-generating brain tissue, i.e. the seizure onset zone (SOZ), without recording actual seizures could reduce the duration of invasive EEG monitoring for patients with drug-resistant epilepsy. A widely-adopted practice in the literature is to compare the incidence (events/time) of putative pathological electrophysiological biomarkers associated with epileptic brain tissue with the SOZ determined from spontaneous seizures recorded with intracranial EEG, primarily using a single biomarker. Clinical translation of the previous efforts suffers from their inability to generalize across multiple patients because of (a) the inter-patient variability and (b) the temporal variability in the epileptogenic activity. Approach . Here, we report an artificial intelligence-based approach for combining multiple interictal electrophysiological biomarkers and their temporal characteristics as a way of accounting for the above barriers and show that it can reliably identify seizure onset zones in a study cohort of 82 patients who underwent evaluation for drug-resistant epilepsy. Main results . Our investigation provides evidence that utilizing the complementary information provided by multiple electrophysiological biomarkers and their temporal characteristics can significantly improve the localization potential compared to previously published single-biomarker incidence-based approaches, resulting in an average area under ROC curve (AUC) value of 0.73 in a cohort of 82 patients. Our results also suggest that recording durations between 90 min and 2 h are sufficient to localize SOZs with accuracies that may prove clinically relevant. Significance . The successful validation of our approach on a large cohort of 82 patients warrants future investigation on the feasibility of utilizing intra-operative EEG monitoring and artificial intelligence to localize epileptogenic brain tissue. Broadly, our study demonstrates the use of artificial intelligence coupled with careful feature engineering in augmenting clinical decision making.